June 11, 2024, 4:51 a.m. | Aghiles Kebaili, J\'er\^ome Lapuyade-Lahorgue, Pierre Vera, Su Ruan

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.05421v1 Announce Type: cross
Abstract: Despite the increasing use of deep learning in medical image segmentation, the limited availability of annotated training data remains a major challenge due to the time-consuming data acquisition and privacy regulations. In the context of segmentation tasks, providing both medical images and their corresponding target masks is essential. However, conventional data augmentation approaches mainly focus on image synthesis. In this study, we propose a novel slice-based latent diffusion architecture designed to address the complexities of …

abstract acquisition arxiv availability challenge context cs.cv data deep learning diffusion diffusion models eess.iv image improving latent diffusion models major medical mri privacy privacy regulations regulations segmentation slice synthesis tasks training training data type

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